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C2020-011 - IBM SPSS Statistics Level 1 v2 - BrainDump Information

Vendor Name : IBM
Exam Code : C2020-011
Exam Name : IBM SPSS Statistics Level 1 v2
Questions and Answers : 55 Q & A
Updated On : April 18, 2019
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C2020-011 IBM SPSS Statistics Level 1 v2

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C2020-011 exam Dumps Source : IBM SPSS Statistics Level 1 v2

Test Code : C2020-011
Test Name : IBM SPSS Statistics Level 1 v2
Vendor Name : IBM
Q&A : 55 Real Questions

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IBM IBM SPSS Statistics Level

IBM Wins a 2018 crimson Dot Design Award for SPSS information | killexams.com Real Questions and Pass4sure dumps

The IBM Hybrid Cloud group is back at it with yet one other win for design. I’m excited to announce that our design team has been awarded the 2018 red Dot: communique Design Award for IBM SPSS facts within the Interface Design class. This award is a continuation of the design achievements we have considered this previous year, including the A’Design Awards, IF Design Awards, and others. i'm extremely joyful to look the challenging work of our designers and IBM Design continue to shine and make a change in enterprise utility.

First developed within the 1990’s, the purple Dot Award has been the revered overseas seal of brilliant design fine. Designers, businesses, and groups from forty five distinctive nations took half during this yr’s competitors, totaling over 8,600 entries that underwent a 24 member jury.

“All folks that development throughout the difficult adjudication manner to garner a pink Dot have each intent to be proud of themselves, because the jury can provide our award simplest to creations of excessive design quality. This makes me the entire greater delighted to congratulate the laureates in actual fact on their hard-earned success.” — Professor Dr. Peter Zec, founder and CEO of the crimson Dot Award

Receiving this award become enormously interesting for our crew and we are honored to be among the winners. here is a tremendous success for our designers who worked on this product, and they faced a captivating and difficult event in engaged on this product.

what is IBM SPSS?

IBM SPSS records is a powerful facts analysis tool that is one of the most common data purposes. for the reason that its inception in 1968, SPSS data has been revamped and redeveloped diverse times. Now the design crew at IBM has taken on the task of growing a completely fresh user journey.

during this latest remodel of IBM SPSS data, we implemented design thinking concepts through working intently with our users and making bound this modernized version of SPSS facts aligns with their needs. Our most desirable purpose was to create an impressive device that is not handiest easy and intuitive to make use of, however that our clients can have fun with.

Our group and Design method

The IBM SPSS design crew is a component of the IBM Design Studios in Boeblingen, Germany. The group consists of a various community, with many participants originating from diverse international locations and cultures. Some participants of the group had some heritage with data while others had been working in this box for the first time.

Following the principles of IBM Design thinking (observe > reflect > Make), our team applied a remodel that brings a far better center of attention on clients for SPSS information. The design crew conducted intensive research on the person base of SPSS records so as to see how the software can more suitable meet their wants. The existing consumer base levels from much less skilled users comparable to college students to greater expert clients such as records scientists or business specialists. A key insight from the group’s research turned into that much less experienced users had been intimidated both by the math work and the complexity of the utility.

the new designs focused on simplifying workflows, reducing the overall complexity of the UI and interactions, and featuring beginners a simple on-boarding to statistics and to the product. an additional crucial function in the redesign turned into a training book led by a personality named Simon, who serves as an in-utility ebook, helping amateur clients bear in mind different services and obtain their desires quicker.

The crew faced some pleasing challenges in redesigning a made from such complexity, and one that has additionally been round for so decades. a big success of the designers turned into making the product purchasable and tasty to new users devoid of alienating decade-long, experienced users.

a glance Into the Future

The preview version of our new IBM SPSS statistics experience was released in March 2018, and made accessible to the general public as a trial on the IBM consider conference is Las Vegas, and on account that June 26 , the new UI is commonly purchasable to all SPSS statistics subscribers. This preview is barely the initial step, providing the most used statistical analyses, and fundamental capabilities for facts training, for presentation and for reporting effects. Over here months the team might be working so as to add more elements and capabilities with a purpose to meet adventure needs of all of our person agencies.

no longer just Updating — Redesigning

i'm so extremely joyful to see yet another Hybrid Cloud design crew acquire a global award for his or her work. IBM SPSS information is yet a different illustration of how design is making an immense change within the success of our items. As we continue to use design to create greater relatable and effective items, we are able to provide our clients the experiences that they need and wish. I’m thrilled and proud to observe the difference that our design group is making on the planet of business software, and that i can’t wait to look how we proceed to impact the lives of our users.

Award Winners:
  • Design supervisor: Caroline legislation
  • Design Leads: Dirk Willuhn and Eva Cochet-Weinandt
  • Design crew: Christian Fritsche, Dimitri Hoffmann, Jaehee (Chloe) Lee, Oleksandr Sabov, Stephan Feger
  • thanks to these contributing designers: Katrin Ellice Heintze, Leila Johannesen, Marion Bruells, Phil Brucker, Robin Auer, Sammy Schuckert, Stefan Schwarz
  • Design interns: Mengzhu Deng, Nathalie Mader, Ting-Hao (Howard) Huang, Vanessa Ng

  • IBM SPSS facts Licenses Renewed | killexams.com Real Questions and Pass4sure dumps

    Our annual license for IBM SPSS facts has been renewed, and all licenses bought between these days and can 1, 2015 will expire on July 31, 2015.

    if you purchased a license between may additionally 1, 2014 and these days, you have been despatched renewal authorization codes by way of electronic mail that may be used to lengthen the expiration date of your utility.

    IBM is not renewing edition 19 this 12 months, and any one the usage of that version (or earlier) will should improve to a more recent version.  IBM SPSS facts licenses can be found for purchase via OIT for $75 per laptop per yr.


    The Ministry of Justice Selects Predictive Analytics from IBM SPSS | killexams.com Real Questions and Pass4sure dumps

    The Ministry of Justice is the usage of predictive analytics expertise from SPSS, an IBM company, to assess the probability of prisoners re-offending on their liberate and to subsequently aid enhance public protection.

    some of the UK's largest government departments with greater than ninety five,000 personnel and a price range of GBP9.2 billion, the Ministry of Justice is the use of predictive analytics to check the facts held within its wrongdoer evaluation device (OASys). moreover, the analysis is assisting the Ministry of Justice increase treatment ambitions for prisoners all over their sentence to cut back the chance they're going to commit crimes upon their liberate.

    The OASys system is used across approximately 140 prisons and all probation areas in England and Wales and facts tips from over 3.4 million prisoner assessments. This comprises information on individual perpetrator circumstances akin to lodging, training, relationships, fiscal administration and earnings, subculture and associates, drug and alcohol misuse, emotional well-being, behaviour and attitudes.

    These facts are being used for quantitative evaluation that could determine patterns inside the facts. The resulting intelligence types the basis for more desirable measurements of perpetrator possibility and need. each the OASys Violence Predictor and OASys time-honored Re-offending Predictor have helped to greatly improve predictions about re-offending. in the case of violent crime, the prediction about re-offending has more advantageous from sixty eight per cent to 74 per cent while the prediction about re-offending when it comes to established offences more advantageous from 76 per cent to 80 per cent.With Predictive expertise from IBM SPSS, the Ministry of Justice is analysing hidden tendencies and patterns inside the facts. as an example: the technology helps establish whether offenders with specific issues akin to drug and alcohol misuse are more likely to re-offend than other prisoners.

    "With just about 4 million information on file it without problems would not be possible to trawl via this data manually in an try to identify these factors that may also mean a prisoner is probably going to reoffend," spoke of a spokesperson from the Ministry of Justice. "SPSS' expertise gives us constructive insight into offender records assisting us predict who can also re-offend and enabling us to advise on preventative measures, such as applicable courses addressing perpetrator behaviour before a prisoner's liberate date."

    "the manner the Ministry of Justice is the usage of our technology showcases the high-level capabilities of our application in featuring analytical predictions upon which actions can also be taken ," defined Ian Warner, Public Sector advisor at SPSS, an IBM company. "with the aid of deciding upon trends and patterns hidden within the records, SPSS is helping the Ministry of Justice predict re-offending costs and take steps to satisfy govt ambitions to enrich public safety via cutting back the danger to the typical public and work in opposition t a wiser executive.

    "present day business leaders should move past intuition to insight, from gut suppose to information - as it is now viable to look key patterns in significant quantities of records, to extract essential insights and movement to a new stage of intelligence," Warner referred to. "IBM SPSS predictive analytics is assisting executive organizations everywhere to seriously change the manner they operate through making smarter decisions and improve citizen provider."

    the new predictive analytics device is powered by way of IBM SPSS statistics software, IBM SPSS Modeler facts mining workbench and IBM SPSS textual content Analytics utility.

    The contemporary announcements are part of IBM's ongoing center of attention on assisting consumers use their suggestions as a strategic asset through enterprise Analytics and Optimisation. IBM lately created a new enterprise Analytics & Optimisation capabilities employer, with 4,000 consultants who can help customers arise and working with deep analytics capabilities and made invested more than $12 Billion in biological growth an innovation to additional construct its analytics portfolio.


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    Technology Infrastructure, Graphics and Visualization, and Adaptive Technologies | killexams.com real questions and Pass4sure dumps

    Technology Infrastructure, Graphics and Visualization, and Adaptive Technologies

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    Criterion validity and test-retest reliability of SED-GIH, a single item question for assessment of daily sitting time | killexams.com real questions and Pass4sure dumps

    The aim of the current study was to investigate the criterion validity and test-retest reliability of the SED-GIH question using activPAL3 micro as the criterion measure. The main findings were a moderate correlation (r = 0.31, CI = 0.20–0.41) and a poor agreement (weighted Kappa 0.12, CI = 0.05–0.18) between SED-GIH and activPAL derived sitting time (activPAL-SIT). Significant differences in activPAL-SIT existed between individuals in the different categorical answer options of SED-GIH. The reliability of SED-GIH was excellent (ICC = 0.86, CI = 0.79–0.90) with a substantial agreement (weighted Kappa 0.77, CI = 0.68–0.86).

    The TASST framework was developed to gain an overview of tools used for assessing sedentary behaviour, and categorized them into four domains: type of assessment, recall period, temporal unit and assessment period. According to TASST, SED-GIH is defined as a single item direct measure of sitting, for an unanchored recall period with a temporal unit of a day, and an non-defined assessment period (taxon 1.1.1/2.4/3.1/4.5) [14]. The moderate correlation between sitting time measured objectively with activPAL and sitting time measured subjectively using the SED-GIH question is in line with other questionnaires. IPAQ (International Physical Activity Questionnaire, (TASST taxon 1.1.1/2.2/3.1/4.3) contains three specific sitting items, which have been validated using activPAL. For sitting time during weekdays, including transportation, correlation was low (r = 0.16, ICC = 0.15) and non-significant (p = 0.2) between the two methods. Here, IPAQ underestimated sitting time by 2.2 h per day [19]. PAST (Past-day Adults Sedentary Time, TASST taxon 1.2.2.1/2.1/3.1/4.5) and PAST-U (modified version of PAST, TASST taxon 1.2.2.1/2.1/3.1/4.5) asks participants to report their time spent sitting or lying during the previous day. When using activPAL (version 3) as criterion measure, the validity for PAST was assessed to be r = 0.57 [20], and PAST-U ICC = 0.64 [21]. When Busschaert and co-workers tested the validity of three different questionnaires measuring context-specific sedentary behaviour (TASST taxon 1.2.2.1/2.2/3.1/4.3, 1.2.2.1/NA/NA/NA, 1.2.2.1/2.4/3.1/4.3) they found weak to acceptable validity for adults (r = 0.06–0.52) and older adults (r = 0.38–0.50) [22]. This implies that the SED-GIH has stronger associations with objective sitting than other single item questionnaires, such as IPAQ, when compared to direct measurement. However, these associations are not as strong as the time-specified PAST and PAST-U, which collect information on sitting during the previous day only.

    Participants who estimated their sitting as ≤3 h using SED-GIH, all underestimated their sitting time as compared to activPAL-SIT (see Table 3). Furthermore, participants who estimated their sitting as ≥13 h almost all overestimated their sitting time. These results are in line with comparisons between PAST and activPAL (version 3) derived sitting times. PAST underestimated sitting times at low levels of sitting, and overestimated sitting time at high levels of sitting [20]. However, a Bland Altman between IPAQ and activPAL indicated that IPAQ underestimated sitting time by up to 2.2 h per day (during a total week including transportation) [19], and both PAST-U and the three different questionnaires measuring context-specific sedentary behaviour overestimated sedentary time, with activPAL as the criterion measure [21, 22]. Dall and colleagues concluded that most sitting questionnaires underestimate sitting time by 2–4 h per day. Single item questionnaires are more likely to underestimate sitting time, while questionnaires assessing sitting during a sum of sedentary behaviours using a composite of several items tend to overestimate sitting time. Questionnaires assessing sitting during a sum of sedentary behaviours over an unanchored or longer period of time tend to report larger underestimations [14]. According to this study, the reasons for sitting time underestimations by the SED-GIH question can be explained by it being based on a single-item question during an unanchored period of time.

    The original seven SED-GIH answer categories were collapsed into five, since there were very few participants choosing “Virtually all day” or “Never”. The intention of including all seven answering options was that “Virtually all day” and “Never” might be easier to relate to instead of < 1 h and > 15 h. They also provide the answer options with some anchorage. When the five categories were analysed, the mean values (displayed in Table 1) of sitting time measured with activPAL did not differ much between the categorical answer options of SED-GIH (varying from 8.7 to 10.3 h per day, mean 9.7 h per day). Thus, the objectively measured average sitting time per day had a narrow distribution, even though the participants subjectively estimated their sitting time with SED-GIH in a wider range. However, the accuracy of SED-GIH changed when only two categories were used (more or less than 10 h of sitting per day). The majority of the participants who rated themselves as sitting for 10 h or more, actually sat for more than 10 h (56.3%). The low sensitivity and specificity of SED-GIH indicates that it would not be useful for identifying hazardous sitters (≥ 10 h per day). Objective measurements may be more useful in detecting sedentary behaviour, possibly in combination with PAST or similar questionnaires. More research is thus needed to develop questionnaires assessing sedentary behaviour and provide better outcomes together with objective methods.

    Test-retest reliability of SED-GIH was excellent (ICC = 0.86, CI = 0.79–0.90), which is better than other reliability tested questionnaires. PAST had fair to good reliability (ICC = 0.50), and three different questionnaires measuring context-specific sedentary behaviour had good reliability for adults (ICC = 0.73–0.77) and older adults (ICC = 0.68–0.80) [20, 22]. However, SED-GIH is a single item questionnaire, whereas PAST and the three different questionnaires measuring context-specific sedentary consist of several questions, which can affect test-retest reliability. With a tool consisting of a single item question, it might be easier to answer the same question twice compared to tools consisting of several questions. Thus, SED-GIH has good repeatability and generates reliable answers among older adults. However, it is not known whether SED-GIH can detect changes of sedentary behaviour over time, such as before and after a behavioural change intervention period. This field needs further research.

    Limitations to the current study have been observed in the methods and the processing of the data. Participants may have become more conscious about their habits regarding sitting time when they answered the web questionnaire prior to the objective measures, which may have affected their sitting habits during the week of measurement with activPAL. Additionally, the measurement period between answering SED-GIH and wearing the activPAL varied (mean 16 days ±14 days), which may have affected the agreement. One impact on internal validity is the accuracy of the participants’ dedication to fill in the diary log correctly, which can affect the whole dataset. In the validity study, participants were employees with an office-based job, which is not representative of a general population. SED-GIH should be validated in other contexts and with different populations. In the reliability part of the current study, all participants were elderly. This may have an effect on the results since some elderly persons can have reduced memory function compared to younger adults.

    Implications

    SED-GIH may be useful as a tool when identifying sitting time as a determinant for health risks on a population level, but would not in itself be sufficiently informative for screening for unhealthy sitting habits in primary care. More studies performed on a broader population are needed.


    Associations of ADL and IADL disability with physical and mental dimensions of quality of life in people aged 75 years and older | killexams.com real questions and Pass4sure dumps

    Introduction

    Quality of life has been defined by the World Health Organization Quality of Life Group as “an individual’s perception of their position in life in the context of the culture and value system in which they live and in relation to their goals, expectations, standards and concerns” (World Health Organization Quality of Life Group, 1995, p. 1405). Quality of life in community-dwelling older people predicts the adverse outcomes of institutionalization and premature death, even after controlling for disability and frailty (Bilotta et al., 2011). To support independent living in older people, both health care and social care professionals may need to carry out preventive interventions focused on aspects related to quality of life, with the aim of delaying institutionalization and avoiding premature death. Determining the influence of disability on quality of life in older people is important to developing early detection of problems and conducting preventive interventions.

    In addition to lower quality of life, disability is a relevant health outcome for older persons. There are several ways of defining disability. The most widely used is: experiencing difficulty in carrying out activities that are essential to independent living - difficulties in performing activities of daily living (ADL), and/or instrumental activities of daily living (IADL) (Tas et al., 2007a; Tas et al., 2007b). ADL functions are essential for an individual’s self- care (e.g., wash and dry your whole body and get on and off the toilet), whereas IADL functions are more concerned with self-reliant functioning in a given environment (e.g., prepare dinner and do the shopping). ADL disability represents a more severe and later form of disability than IADL disability (Hardy et al., 2005; Wong et al., 2010), resulting in a lower proportion of persons with ADL disability than IADL disability (Akosile et al., 2018; Chatterji et al., 2015).

    Disability is associated with increased health care utilization and related costs (Fried et al., 2004), and premature death (Manton, 1988; Mor et al., 1994; Walter et al., 2001). In addition, disability is associated with impaired quality of life in older people (Akosile et al., 2018; Den Ouden et al., 2013; Gureje et al., 2006; Soósová, 2016). However, the “disability paradox” (Albrecht & Devlieger, 1999) suggests that persons with severe impairments may nevertheless report high quality of life (Watson, 2002), although this paradox seems to dissolve when contextual factors (i.e., personal and environmental situation) are considered (Fellinghauer et al., 2012). Disability has a dynamic nature, so persons can move in and out of disability, with transitions between states of disability (Hardy et al., 2005; Nikolova et al., 2011; Van Houwelingen et al., 2014). Transitions to greater disability were more common than improvements in disability in people aged ≥85 years (Van Houwelingen et al., 2014). In particular, people with more than one chronic disease, depressive symptoms, and cognitive impairment had the highest risk of deteriorating; however, a small number of very old people are able to improve in their disability status (Van Houwelingen et al., 2014).

    The aim of the present cross-sectional study was to determine the influence of both ADL and IADL disability on quality of life, incorporating a physical and a mental dimension, in people aged 75 years and older. In contrast to previous research (Akosile et al., 2018; Den Ouden et al., 2013; Gureje et al., 2006; Soósová, 2016) the main focus here is on the associations between ADL and IADL items and quality of life. Items are more concrete than the types of disability (ADL, IADL) and thereby provide health care professionals (e.g., nurses, general practitioners, physiotherapists) and professionals working in the social domain (e.g., social workers, domestic help) specific targets to enhance quality of life in older people. To enhance quality of life of older people, it is relevant to know which items of ADL and IADL are associated with lower quality of life because the interventions will be carried out by different professionals. For example, if an older person can no longer wash and undress themselves, a district nurse can provide support (e.g., in the Netherlands) and if an older person has difficulties performing household activities then domestic help can provide the necessary support.

    Methods Study population and data collection

    The Senioren Barometer is a web-based questionnaire used to assess the opinion of a panel of Dutch older people (aged 50 years and older) about different aspects of life. This questionnaire has been used in previous studies (Gobbens, Luijkx & Van Assen, 2013; Gobbens, Van Assen & Schalk, 2014).

    In the period from December 2009 to January 2010 1,492 respondents completed at least part of the questionnaire, of whom 1,031 filled out the section on background characteristics, quality of life, and disability. Because disability is associated with greater age (Tas et al., 2007a; Tas et al., 2007b) the author selected only people aged 75 years and older (n = 377). As described in previous studies using the Senioren Barometer, older people can volunteer, and participation is always without obligation. The sample was invited to participate in the study in different ways and through multiple sources. First, people could indicate through the website (http://www.seniorenbarometer.nl) that they wanted to complete the questionnaire. Second, organizations for older people in the Netherlands were approached and asked to issue an announcement of the study on their websites so that their members who were interested could register. Third, a major source of participants was persons who attended computer training courses for older persons given by a large training and educational institute in the Netherlands.

    Medical ethics approval was not necessary as particular treatments or interventions were not offered or withheld from respondents. The integrity of respondents was not encroached upon as a consequence of participating in the study, which is the main criterion in medical-ethical procedures in the Netherlands (Central Committee on Research inv. Human Subjects, 2010). Informed consent in relation to detailing the study and maintaining confidentiality was observed.

    Measures Quality of life

    The author used the Short-Form Health Survey (SF-12) for measuring quality of life (Ware Jr, Kosinski & Keller, 1996). The SF-12 is a shorter version of the SF-36 (Ware Jr & Sherbourne, 1992) that uses only 12 questions. The SF-12 is developed to replicate the SF-36 with the aim to minimalize respondent burden. The 12 items are used to derive two summary quality of life measures, the physical dimension (six items) and the mental dimension (six items); their scores range from 0 to 100. Higher scores refer to better quality of life. Several studies have reported the validity and reliability of the SF-12 as a measure of quality of life in the general population, including older people (Bentur & King, 2010; Cernin et al., 2010; Jakobsson et al., 2012; Kim et al., 2014; Kontodimopoulos et al., 2007). In the present study, the (unstandardized) Cronbach’s alpha was .82 for the physical dimension and .73 for the mental dimension; an adequate value of the Cronbach’s alpha is between .70 and .90 (Cortina, 1993).

    Disability

    The author used the Groningen Activity Restriction Scale (GARS) for assessing disability (Kempen & Suurmeijer, 1990). The GARS is a self-report questionnaire consisting of two subscales. The first subscale measures ADL (11 items) and the second subscale relates to IADL (seven items). Each item has four response options: (1) able to perform the activity without any difficulty, (2) able to perform the activity with some difficulty, (3) able to perform the activity with great difficulty, and (4) unable to perform the activity independently. A distinction can then be made in two categories, complete independence and dependency (more or less). The disability total score ranges from 18 (no disability) to 72 (maximum disability). Following Ormel et al. (2002) the cut-point of 29 has been chosen for the disabled group because this cut-point corresponds with the 85th percentile of the GARS in a large sample of older people (Kempen et al., 1996b). The scores for the ADL and IADL subscales range from 11 to 44 and 7 to 28, respectively, with higher scores indicating greater disability; cut-points for these subscales do not exist. The GARS has shown good psychometric properties for assessing disability in older people (Kempen et al., 1996a). In this study, the (unstandardized) Cronbach’s alpha’s for ADL and IADL disability were .82 and .80, respectively, representing adequate values (Cortina, 1993)

    Background characteristics: sociodemographic and multimorbidity

    The sociodemographic background characteristics considered were age, sex, marital status, education level, and net household income. See Table 1 for a detailed description of the answer categories. Multimorbidity was assessed by asking the respondents, “Do you have two or more diseases and/or chronic disorders?” (yes/no).

    Analysis strategies

    First, the author determined the characteristics of the sample using descriptive statistics. Second, the quality of life dimensions (physical, mental) scores for non-disabled and disabled participants were compared using student’s t–tests assuming unequal population variances. Effect size was assessed with Cohen’s d, assuming equal population variances; .2, .5, .8 corresponding to small, medium, large effect size, respectively (Cohen, 1988). Correlations of ADL and IADL disability with the physical and the mental dimensions of the SF-12 were also examined. According to Cohen, correlations were considered as small, medium, or large with coefficients of .1, .3, or .5, respectively (Cohen, 1988).

    Table 1:

    Participant characteristics (N = 377).

    Characteristic n(%) Age, mean ± SD, range 79.8 ± 3.7, 75–95 Sex, % of men 261 (69.2) Marital status Married or cohabiting 244 (64.8) Single 36 (9.5) Divorced 11 (2.9) Living apart together 3 (0.8) Widowed 83 (22.0) Education None 30 (8.0) Primary 34 (9.0) Secondary 160 (42.4) Polytechnics and higher vocational training 113 (30.0) University 40 (10.6) Incomea €999 - or less 7 (2.1) €1,000–€1,499 44 (13.2) €1,500-€1,999 54 (16.1) €2,000–€2,499 90 (27.0) €2,500–€2,999,- 54 (16.1) €3,000–€3,499,- 38 (11.4) €3,500–€3,999,- 25 (7.5) €4,000–€4,499,- 11 (3.3) €5,000 or more 11 (3.3) Multimorbidity, % yes 166 (44.0) GARS Total disability 95 (25.2) ADL disability, mean ± SD, range 13.6 ± 3.8,11–33 Dress yourself 55 (14.6) Get in and out of bed 31 (8.2) Stand up from sitting in a chair 53 (14.1) Wash your face and hands 6 (1.6) Wash and dry your whole body 57 (15.1) Get on and off the toilet 12 (3.2) Feed yourself 4 (1.1) Get around in the house (if necessary, with a cane) 18 (4.8) Go up and down the stairs 134 (35.5) Walk outdoors (if necessary, with a cane) 75 (19.9) Take care of your feet and toenails 183 (48.5) IADL disability, mean ± SD, range 11.2 ± 4.5, 7–28 Prepare breakfast or lunch 14 (3.7) Prepare dinner 88 (23.3) Do “light” household activities 69 (18.3) Do “heavy” household activities 212 (56.2) Wash and iron your clothes 169 (44.8) Make the beds 185 (49.1) Do the shopping 86 (22.8) SF-12 Physical dimension of quality of life, mean ± SD, range 66.9 ± 25.6, 0–100 Mental dimension of quality of life, mean ± SD, range 74.5 ± 18.7, 10–100

    Before carrying out regression analyses some sociodemographic variables were coded for analysis. As in a previous study, the author created dummies for sex (“1” woman, “0” man), marital status (“1” married or cohabiting, “0” rest) and multimorbidity (“1” yes, “0” no), and linear effects of age and level of education were incorporated into the analyses (Gobbens, Luijkx & Van Assen, 2013). Bivariate associations between one background variable or disability item on the one hand and one quality of life dimension (physical, mental) on the other hand were tested using regression analyses. Subsequently, the author examined the effects of each variable (background variables, disability items) on the physical and mental dimensions in four multiple linear regression analyses, controlling for all the other variables in the model. The simplest model only assessed the effects of all background variables together. One model also included all 11 ADL disability items, whereas another model also included the seven IADL items together with the background variables. The most complex model included all 24 items. The fit (explained variance) of all four models was tested (R2) and compared (delta R2). Power analyses using GPower 3.1.0 (Faul et al., 2007) showed that the sequential linear regression analyses on 377 participants had a power of at least 80% to detect an effect of Cohen’s f2 = .056 which is a small to medium effect size (Cohen, 1988).

    Data were processed using SPSS version 24.0 (IBM Corporation, Armonk, NY, USA). All reported p-values are two-tailed. A p-value <0.05 was considered statistically significant.

    Results Participant characteristics

    See Table 1 for an overview of the descriptive statistics of the participant characteristics. The mean age of the participants was 79.8 (SD = 3.7), 69.2% were male, and 64.8% were married or cohabiting. The average scores on quality of life for the physical and mental dimensions were 66.9 (SD 25.6) and 74.5 (SD 18.7), respectively. Using the cut-point of 29 on the GARS, 25.2% of the participants were totally disabled, including both the ADL and the IADL subscale. In addition, 54.6% and 67.4% of the participants had at least one ADL disability and IADL disability, respectively. Of the 11 ADL disability items, participants experienced the greatest dependency in relation to taking care of their feet and toenails (48.5%). Of the 7 IADL disability items, participants experienced the greatest dependency in relation to doing “heavy” household activities (56.2%). In general, it should be noted that the percentages of the IADL disability items are higher than the percentages of the ADL disability items; five IADL disability items scored higher than 20% versus two ADL disability items (see Table 1).

    Differences between non-disabled and disabled participants on quality of life

    Table 2 presents the results of comparing disabled and non-disabled people on the physical and the mental dimensions of the SF-12. Disabled participants scored lower on both quality of life dimensions (p-values < 0.001), with very large effect sizes, d = 1.30 for the mental dimension and d = 1.82 for the physical dimension.

    Table 2:

    Comparison of quality of life dimensions between disabled and non-disabled participants.

    Non-disabled n = 279 M (SD) Disabled n = 95 M (SD) Results t-testa Effect size Cohen’s db Physical dimension of quality of life 76.19 (19.29) 39.61 (22.27) t(144.95) = 14.29 < 0.001 d = 1.82 Mental dimension of quality of life 79.85 (14.59) 58.68 (20.46) t(128.05) = 9.31 < 0.001 d = 1.30 Correlations between disability and quality of life

    Table 3 shows the correlations between ADL disability, IADL disability, physical quality of life, and mental quality of life. Most correlations were strong (>5); only the correlation between ADL disability and mental quality of life could be considered as medium (.483) (all p-values < 0.001).

    Table 3:

    Correlations between ADL disability, IADL disability, physical and mental dimensions of quality of life.

    IADL disability Physical quality of life Mental quality of life ADL disability 0.702 −0.683 −0.483 IADL disability −0.676 −0.541 Physical quality of life 0.734 Regression analyses: effects of ADL and IADL disability items on quality of life

    Table 4 presents the results of the bivariate and sequential linear regression analyses on the physical and mental quality of life dimensions of the SF-12. The table shows the effects of six background characteristics, 11 ADL disability items, and seven IADL items on the two dimensions of quality of life (physical, mental). Columns 2–4 and 8–10 present the bivariate regressions. Being a man, younger age, married or cohabiting, higher education, higher income, and no multimorbidity were associated with higher scores on both the physical and mental dimensions. Of the 11 ADL disability items, all were associated with physical quality of life and 10 were associated with mental quality of life. The exception was the item “feed yourself” (p = 0.058). All seven IADL disability items were associated with both quality of life dimensions.

    Table 4:

    Effect of background characteristics, ADL and IADL disability items on the physical and mental dimensions of quality of life.

    Physical dimension of quality of life Mental dimension of quality of life Bivariate Multiple Bivariate Multiple B SE p B SE p B SE p B SE p Background characteristics Sex (women) −11.92 2.81 <0.001 −2.81 2.15 0.192 −4.89 2.08 0.019 2.26 2.10 0.283 Age −0.73 0.36 0.040 0.27 0.23 0.251 −0.81 0.26 0.002 −0.06 0.23 0.801 Marital status (married) 8.48 2.74 0.002 −0.83 2.08 0.692 5.18 2.01 0.010 0.99 2.04 0.626 Education 4.86 1.26 <0.001 0.62 0.88 0.481 2.86 0.92 0.002 0.22 0.86 0.795 Income 3.60 0.74 <0.001 0.33 0.52 0.524 1.92 0.52 <0.001 0.35 0.51 0.496 Multimorbidity −29.13 2.21 <0.001 −13.35 1.82 <0.001 −13.50 1.82 <0.001 −4.03 1.78 0.024 ΔR2 0.364 <0.001 0.162 <0.001 ADL disability items Dress yourself −27.20 2.45 <0.001 −6.98 3.09 0.024 −12.63 1.96 <0.001 −0.12 3.02 0.967 Get in and out of bed −32.04 3.72 <0.001 −6.95 3.42 0.043 −17.72 2.82 <0.001 −6.57 3.34 0.050 Stand up from sitting in a chair 26.94 2.90 <0.001 −5.68 2.59 0.029 −16.63 2.19 <0.001 −5.55 2.53 0.029 Wash your face and hands −27.89 5.92 <0.001 2.07 5.50 0.707 −12.96 4.39 0.003 2.70 5.37 0.615 Wash and dry your whole body −24.35 2.19 <0.001 2.01 3.10 0.516 −12.28 1.73 <0.001 0.95 3.03 0.754 Get on and off the toilet −37.37 7.27 <0.001 0.99 4.94 0.841 −25.07 5.33 <0.001 −4.28 4.82 0.375 Feed yourself −40.77 12.72 0.001 −11.28 9.22 0.222 −17.79 9.36 0.058 −3.12 9.00 0.729 Get around in the house (if necessary, with a cane) −24.78 4.24 <0.001 6.43 3.37 0.057 −13.93 3.15 <0.001 1.33 3.29 0.686 Go up and down the stairs −21.00 1.23 <0.001 −5.78 1.63 <0.001 −10.62 1.07 <0.001 −0.84 1.59 0.597 Walk outdoors (if necessary, with a cane) −20.33 1.60 <0.001 0.03 1.82 0.985 −11.03 1.28 <0.001 0.93 1.77 0.601 Take care of your feet and toenails −10.80 0.88 <0.001 1.41 0.86 0.101 −5.29 0.71 <0.001 1.35 0.83 0.108 ΔR2 0.058 <0.001 0.045 0.012 IADL disability items Prepare breakfast or lunch −14.34 4.11 0.001 6.83 3.34 0.042 −7.97 3.01 0.009 2.07 3.26 0.526 Prepare dinner −5.89 1.42 <0.001 0.74 1.15 0.517 −3.29 1.05 0.002 0.003 1.12 0.998 Do “light” household activities −19.29 1.74 <0.001 −1.52 1.68 0.368 −9.83 1.37 <0.001 0.83 1.64 0.613 Do “heavy” household activities −14.27 0.75 <0.001 −6.57 1.05 <0.001 −8.64 0.62 <0.001 −4.55 1.03 <0.001 Wash and iron your clothes −7.49 1.05 <0.001 −1.03 0.91 0.260 −4.03 0.79 <0.001 0.58 0.89 0.516 Make the beds −13.05 0.88 <0.001 −1.23 1.07 0.254 −7.90 0.70 <0.001 −1.83 1.05 0.083 Do the shopping −18.06 1.37 <0.001 −5.41 1.42 <0.001 −11.13 1.06 <0.001 −5.74 1.39 <0.001 ΔR2 0.108 <0.001 0.135 <0.001 ΔR2 ADL and IADL 0.350 <0.001 0.282 <0.001 R2 total 0.714 <0.001 0.444 <0.001

    Columns 5–7 and 11–13 summarize the results of the sequential linear regression analyses. R2 total indicates that 71.4% and 44.4% of the physical and mental quality of life dimensions were explained by all the predictors together, respectively. After controlling for the background variables (sociodemographic characteristics, multimorbidity), disability (ADL and IADL items together) explained 35.0% of physical quality of life and 28.2% of mental quality of life, with both p-values <0.001. The ADL disability items together explained 5.8% and 4.5% of the physical and mental dimension, with p-values <0.001 and 0.012, respectively, after controlling for all background characteristics and IADL disability items, representing a medium to large effect size (f2 = .20) and a small to medium effect size (f2 = .08), respectively. The IADL disability items together explained a significant part of both quality of life dimensions after controlling for background characteristics and ADL items, with increases in explained variance of 10.8% (physical; f2 = .38, large effect size) and 13.5% (mental; f2 = .24, medium to large effect size) (both p-values <  0.001).

    In addition, Table 4 presents the effects of each of the background characteristics and individual ADL and IADL items on physical and mental quality of life. The columns five and 11 show the regression coefficients with corresponding standard errors (columns six and 12) and p-values (columns seven and 13).

    Before interpreting the effects of individual items after controlling for the other variables, the author checked for multicollinearity. As the variance inflation factors (VIF) for all items were smaller than 5, which is below the threshold of 10 (Yu, Jiang & Land, 2015), the author relied on his estimates as they are not strongly affected by multicollinearity.

    Of the background variables, only multimorbidity was negatively associated with quality of life, both physical and mental. None of the other background characteristics were associated with quality of life, after controlling for all the other variables in the model.

    Of the 11 ADL disability items only four were significantly associated with quality of life. The ADL item “stand up from sitting in a chair” was negatively associated with both dimensions (physical, mental). The ADL items “dress yourself”, “get in and out of bed”, and “go up and down the stairs” were only negatively associated with the physical dimension of quality of life. Of the seven IADL disability items, three were associated with quality of life. The two IADL items (do “heavy” household activities, do the shopping) were negatively associated with both the physical and mental dimensions of quality of life and “prepare breakfast or lunch” was positively associated with the physical dimension. All effect sizes (f2) of the individual ADL and IADL disability items on physical as well as mental quality of life were <.15, representing small effect sizes. Of all ADL disability items, “go up and down the stairs” and “stand up from sitting in a chair” had the largest effect sizes on the physical and mental quality of life dimensions, f2 = .042 and f2 = .016, respectively. Of all IADL disability items, the item with the largest effect sizes on the physical as well as the mental dimension of quality of life was “do “heavy” household activities”, with f2 = .13 and .065, respectively.

    Discussion

    In this study the author determined the associations between ADL and IADL disability items and quality of life in a sample consisting of 377 Dutch people aged 75 years or older. The author used two validated questionnaires, the GARS for assessing disability and the SF-12 for assessing quality of life, containing a physical and a mental dimension. To the best of my knowledge, the present study was the first using the GARS and the SF-12 to determine the associations between disability and quality of life. In addition, no previous study paid attention to the predictive value of the individual ADL and IADL disability items on quality of life.

    The bivariate regression analyses showed that the following factors were associated with physical quality of life as well as mental quality of life: being a man, younger age, married or cohabiting, higher education, higher income, no multimorbidity, ten ADL disability items, and seven IADL disability items. The ADL disability item “feed yourself” was not associated with the mental dimension. However, the sequential linear regression analyses revealed that only multimorbidity, ADL item “stand up from sitting in a chair”, and IADL items “do ‘heavy’ household activities” and “do the shopping” were significantly associated with both quality of life dimensions, after controlling for all the variables in the model.

    The finding that multimorbidity is associated with lower quality of life in older people is supported by previous studies in several countries using different measurement instruments (Brettschneider et al., 2013; Fortin et al., 2006; Garin et al., 2014; Gu et al., 2018). In Germany, quality of life of multimorbid people aged 65 to 85, assessed with the EQ-5D and the EQ-5D visual analogue scale (EQ-VAS) (Rabin & De Charro, 2001), decreased with an increasing count and severity of chronic conditions (Brettschneider et al., 2013). In Canada, 238 people completed the SF-36 (Ware Jr & Sherbourne, 1992) for assessing quality of life, and multimorbidity was measured by counting the number of chronic diseases and with the Cumulative Illness Rating Scale (CIRS) (Linn, Linn & Gurel, 1968); this study showed that the physical health dimension of quality of life deteriorated more than the mental health dimension of quality of life with increasing multimorbidity (Fortin et al., 2006). A study among Spanish people (≥50 years) also demonstrated that the number of chronic diseases was associated with lower quality of life (Garin et al., 2014), assessed with the WHOQOL-AGE (Caballero et al., 2013). Finally, a longitudinal study conducted in China showed that distinct multimorbidity patterns had various impacts on different dimensions of quality of life among community-dwelling older people (Gu et al., 2018). These findings are important because multimorbidity is frequently present in older people; in the age group 75–84 years the prevalence is 71.7% (Abad-Diez et al., 2014). The author recommends more studies focusing on the impact of multimorbidity patterns on quality of life in other countries. These studies should focus in particular on effects of combinations of common chronic diseases on quality of life, thereby providing direction to (preventive) interventions.

    All ADL disability items combined explained a significant part of the variance of both the physical dimension and the mental dimension of quality of life. Another study showed that maintaining independence in ADL had a positive effect on four domains of the WHOQOL-OLD (sensory abilities; autonomy; past, present, and future activities; social participation) (Power, Quinn & Schmidt, 2005), and one domain of the WHOQOL-BREF (physical health) (The WHOQOL Group, 1998; Soósová, 2016)). Quality of life, assessed with the WHOQOL-OLD (Power, Quinn & Schmidt, 2005) and the WHOQOL-BREF (The WHOQOL Group, 1998), were significantly associated with ADL disability in two samples of Nigerian older people aged 65 years and older (Akosile et al., 2018; Gureje et al., 2006). A Dutch study including a total of 537 middle-aged and older persons also found that quality of life, assessed with the SF-36 (Ware Jr & Sherbourne, 1992), was associated with ADL disability, measured with the Katz-questionnaire (Katz & Akpom, 1976; Den Ouden et al., 2013). In particular, health care professionals (e.g., district nurses, physiotherapists, general practitioners, occupational therapists) should identify (potential) limitations in performing ADL at an early stage in order to maintain or increase quality of life in older people. Based on the present study, special attention is needed to address problems people have when standing from sitting, because this activity is associated with lower physical and mental quality of life.

    All IADL disability items combined explained a larger part of the variance of both the physical and the mental dimension of quality of life compared with all ADL disability items together, 10.8% versus 5.8% and 13.5% versus 4.5%, respectively. Two studies referred to above also found that IADL disability was associated with quality of life (Akosile et al., 2018; Gureje et al., 2006). The finding that IADL disability items were more prevalent than ADL disability is supported by other studies (Akosile et al., 2018; Bleijenberg et al., 2017; Hu et al., 2012) and contributes to the evidence that IADL disability occurs earlier than ADL disability; probably because IADL is more complex and appeals more to cognitive function. In Nigeria the prevalence figure of IADL disability was 39.3% versus ADL disability 32.5% (Akosile et al., 2018). Among Dutch older people, with an average age of 74.6 years, carrying out household tasks was the most frequent problem (44.8%), followed by travelling (26.9%), and grocery shopping (23.0%) (Bleijenberg et al., 2017). In particular, the first and the last item are important because the present study showed that these two items were associated with the physical as well as the mental dimension of quality of life in older people. These findings have not been available to date. Conducting interventions on problems that older people can experience with performing heavy household activities and shopping could help them reach a higher quality of life. Domestic help may meet these needs or additionally reablement or restorative care services may be of benefit. These are short term services aimed at improving the independence of older people so they can hopefully go back to living independently without ongoing assistance.

    The model including all the prediction variables explained a large part of the variance in scores of the physical and mental dimensions of the SF-12, 71.4% and 44.4%, respectively. In a sample of community-dwelling older Dutch people (n = 8,928) it was shown that people experiencing disability, multimorbidity, and frailty scored lower on quality of life compared with people experiencing individual conditions (Lutomski et al., 2014). It is possible that the explained variances in the scores of the quality of life dimensions were also greater if depression as a predictive variable was included in the model; a review, including 74 studies, found an association between depression and lower quality of life in older people, independent of how quality of life was assessed (Sivertsen et al., 2015).

    This study has some limitations. First, the cross-sectional nature of this study does not allow strict cause–effect interpretations of the associations between the ADL and IADL disability items and quality of life. A longitudinal study is recommended to establish such associations. Second, disability was assessed by the GARS, a self-report measure, that does not include performance-based measures. A combination of both measures may be the best way to fully capture the picture of disability in ADL and IADL. However, in a sample of oldest old (≥80 years) it was demonstrated that self-assessments for disability in ADL and IADL reliably reflect direct assessment in performance (Bravell, Zarit & Johansson, 2011). Third, the author used the Senioren Barometer for data collection. This is a web-based questionnaire, so access to Internet was necessary for participating in the present study; this may have led to selection bias. In this context, it should be noted that in the study sample 69.2% were men, while in the Dutch population aged 75 years and older, only 37.9% are men, as established January 1, 2010 (Statistics Netherlands, 2017).

    Conclusions

    In this study the author showed that disability in ADL and IADL is negatively associated with quality of life in older people. Therefore, it is important for health care professionals to carry out interventions aimed at preventing and diminishing disability or its adverse outcomes, such as a lower quality of life. Promising interventions are multidisciplinary and multifactorial in nature, should be preceded by an individualized assessment, and should involve case management and long-term follow up (Daniëls et al., 2010). Lifestyle interventions targeting physical exercise, nutrition, and cognition appear to be effective against disability in ADL and IADL; in order to be actually effective, these interventions should be inexpensive, feasible, and easy to implement (Fougère et al., 2018). In line with the findings of the present study, it is recommended to first focus on the disability items that have the greatest impact on quality of life of older people (“stand up from sitting in a chair”, “do ‘heavy’ household activities” and “do the shopping”) to achieve the best outcome.

    Supplemental Information Raw data exported from the Seniorenbarometer 2009 applied for data analysis and preparation for Tables 1– 4


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